Earth Observation Data Cubes

• Main Author: Camara, Gilberto (auth)
• Other Authors: Minchin, Stuart (auth), Killough, Brian (auth), Giuliani, Gregory (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: Geography; knowledge base; metadata; Synthetic Aperture Radar; versioning; web services; web application; sustainable development goals; earth observations; FAIR principles; land cover classification; semantic enrichment; satellite imagery; imagery; analysis; information extraction; ARD; analysis ready data; swiss DC; data cube; Open Data Cube; UN 2030 Agenda for Sustainable Development; time-series; graph data; Digital Earth Australia; query store; open data cube; pyroSAR; R; earth oberservation; image cube; sentinel; open science; Sentinel; reproducibility; change; big EO data; earth observation; big Earth data; Earth Observations; Australia; geospatial standards; big earth data; visualization; UN System of Environmental Economic Accounting; interferometric coherence; dynamic data citation; intelligent semantic agents; data curation; snow cover; big data; Analysis Ready Data; climate change; topology based map algebra; data provenance; Sentinel-1; Sentinel-2; remote sensing; interoperability; image data cube; optical remote sensing; dual-polarimetric decomposition; GIS; Gran Paradiso National Park; data sharing; SAR; map algebra; Earth observation; Armenian DC; data cubes; Data Cube; data discovery; Earth observation data; persistent identifier; Landsat; GRASS GIS; subset; landsat
• Online Access: DOAB: download the publication; DOAB: description of the publication

Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10